Intelligent Logistics Optimization for Carbon Emission Reduction in Cement Industry: A Sensor-Driven Dynamic Order Matching Approach

Efficient logistics management is critical for reducing carbon emissions in the cement industry. This work proposes an intelligent logistics optimization framework integrating sensordriven vehicle state detection, reinforcement learning-enhanced order matching, and adaptive path optimization. The system employs IMU-based vehicle monitoring, a Deep Double QNetwork
(DDQN)-Hungarian matching algorithm for dynamic order allocation, and a reinforcement learning-guided shortest path strategy combining Dijkstra and A*. Experimental results on real-world logistics data demonstrate reduced empty mileage, improved order matching efficiency, and lower carbon emissions. The proposed approach outperforms traditional methods, offering a scalable and adaptive solution for sustainable cement transportation.